Secure model training is the process of developing machine learning models while protecting sensitive data and preventing security risks. It involves using special methods and tools to make sure private information is not exposed or misused during training. This helps organisations comply with data privacy laws and protect against threats such as data theft or…
A model retraining strategy is a planned approach for updating a machine learning model with new data over time. As more information becomes available or as patterns change, retraining helps keep the model accurate and relevant. The strategy outlines how often to retrain, what data to use, and how to evaluate the improved model before…
A model versioning strategy is a method for tracking and managing different versions of machine learning models as they are developed, tested, and deployed. It helps teams keep organised records of changes, improvements, or fixes made to each model version. This approach prevents confusion, supports collaboration, and allows teams to revert to previous versions if…
Model lifecycle management is the process of overseeing the development, deployment, monitoring, and retirement of machine learning models. It ensures that models are built, tested, deployed, and maintained in a structured way. This approach helps organisations keep their models accurate, reliable, and up-to-date as data or requirements change.
Performance metrics design is the process of deciding which measurements best reflect how well a system, process, or team is achieving its goals. It involves choosing clear, relevant indicators that can be tracked and analysed over time. Good metric design helps organisations understand progress, identify areas for improvement, and make informed decisions.
Churn risk predictive models are tools that help organisations forecast which customers are likely to stop using their products or services. These models use past customer data, such as purchase history, engagement patterns and demographics, to find patterns linked to customer departures. By identifying high-risk customers early, businesses can take steps to improve customer satisfaction…
Reward function engineering is the process of designing and adjusting the rules that guide how an artificial intelligence or robot receives feedback for its actions. The reward function tells the AI what is considered good or bad behaviour, shaping its decision-making to achieve specific goals. Careful design is important because a poorly defined reward function…
Model retraining pipelines are automated systems that regularly update machine learning models with new data. They help ensure that models stay accurate and relevant as real-world conditions change. These pipelines handle tasks such as collecting fresh data, retraining the model, validating its performance, and deploying the updated version.
Model Lifecycle Management is the process of overseeing machine learning or artificial intelligence models from their initial creation through deployment, ongoing monitoring, and eventual retirement. It ensures that models remain accurate, reliable, and relevant as data and business needs change. The process includes stages such as development, testing, deployment, monitoring, updating, and decommissioning.
Time series decomposition is a method used to break down a sequence of data points measured over time into several distinct components. These components typically include the trend, which shows the long-term direction, the seasonality, which reflects repeating patterns, and the residual or noise, which captures random variation. By separating a time series into these…